Barriers to Effective Doctor-Patient Relationship Based on PRECEDE PROCEED Model
Bibliographic record
Abstract
OBJECTIVE: This study intends to investigate interns and faculty members' insights into constructing relationship between physicians and patients at 3 more accredited Iranian universities of medical sciences. METHOD: Applying PRECEDE PROCEED model, semi-structured interviews were completed with 7 interns and 14 faculty members and two themes were emerged from directed content analysis. The meaning units of the first theme, barriers to effective doctor-patient relationship, are discussed in this paper. RESULTS: According to the participants, building doctor-patient relationship is influenced by many contextual and regulatory factors as well as content, process and perceptual skills of physicians. CONCLUSIONS: Faculty and curriculum development, as well as foundation of the department of communication skills at medical schools are recommended to eliminate the impact of poor communication on patients' satisfaction and physicians' self-efficacy specific to their communication skills. PRACTICE IMPLICATIONS: Applying theories and models of health education and health promotion, researchers and educators can use the most predictive constructs of theories to design and implement effective interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".